Deep Dive LS5 eePlumB labels for training set

Purpose

This script takes a deep dive into Landsat 5 labels for a more rigorous analysis of inconsistent band data and outliers in the filtered label dataset. Here we will determine if any more label data points should be removed from the training dataset and whether or not we can glean anything from the metadata in the outlier dataset to be able to pre-emptively toss out scenes when we go to apply the classification algorithm.

harmonize_version = "2024-04-25"
outlier_version = "2024-04-25"

LS5 <- read_rds(paste0("data/labels/harmonized_LS57_labels_", harmonize_version, ".RDS")) %>% 
  filter(mission == "LANDSAT_5")

Check for mis-matched band data between user data and re-pull

Just look at the data to see consistent (or inconsistent) user-pulled data and our pull, here, our user data are in “BX” format and the re-pull is in “SR_BX” format. These are steps to assure data quality if the volunteer didn’t follow the directions explicitly, or if there are differences in the re-pull. The re-pull masks out saturated pixels, so any instance where the “SR_BX” value is NA indicates that the pixel was saturated in at least one band.

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There is some mis-match here, let’s filter any labels where at least one band value doesn’t match between the user pull and the re-pull.

LS5_inconsistent <- LS5 %>% 
  filter(is.na(SR_B7) | B1 != SR_B1 | B2 != SR_B2 | B3 != SR_B3 | 
           B4 != SR_B4 | B5 != SR_B5 | B7 != SR_B7)

LS5_inconsistent %>% 
  group_by(class) %>% 
  summarise(n_labels = n()) %>% 
  kable()

class	n_labels
cloud	214
darkNearShoreSediment	1
lightNearShoreSediment	3
openWater	9
other	1
shorelineContamination	3

Most of these are cloud labels, where the pixel is saturated, and then masked in the re-pull value (resulting in an NA). Let’s drop those from this subset and then look more.

LS5_inconsistent <- LS5_inconsistent %>% 
  filter(!is.na(SR_B7)) 

This leaves 0.6% of the Landsat 7 labels as inconsistent. Let’s do a quick sanity check to make sure that we’ve dropped values that are inconsistent between pulls and where any band value is greater than 1:

LS5_filtered <- LS5 %>% 
  filter(# filter data where the repull data and user data match
         (B1 == SR_B1 & B2 == SR_B2 & B3 == SR_B3 & 
           B4 == SR_B4 & B5 == SR_B5 & B7 == SR_B7),
         # or where any re-pulled band value is greater than 1, which isn't a valid value
         if_all(LS57_ee,
                ~ . <= 1))

And plot:

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And now let’s look at the data by class:

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We aren’t actually modeling “other” (not sufficient observations to classify) or “shorelineContamination” (we’ll use this later to block areas where there is likely shoreline contamination in the AOI), so let’s drop those categories and look at the data again. We’ll also drop the B1-B7 columns here.

LS5_for_class_analysis <- LS5_filtered %>% 
  filter(!(class %in% c("other", "shorelineContamination"))) %>% 
  select(-c(B1:B7))

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Check for systemic volunteer inconsistencies

Let’s also go back and check to see if there is any pattern to the inconsistent labels.

vol_init	n_tot_labs	n_dates
HAD	6	4
BGS	5	2
LRCP	3	2
AMP	1	1

There seem to be just a few inconsistencies here and across multiple dates. This could just be a processing difference (if there happened to be an update to a scene since users pulled these data or if these were on an overlapping portion of two scenes). I’m not concerned about any systemic errors here that might require modified data handling for a specific scene or contributor.

Outlier handling

There are statistical outliers within this dataset and they may impact the interpretation of any statistical testing we do. Let’s see if we can narrow down when those outliers and/or glean anything from the outlier data that may be applicable to the the application of the algorithm. Outliers may be a systemic issue (as in the scene is an outlier), it could be a user issue (a user may have been a bad actor), or they just might be real. This section asks those questions. The “true outliers” that we dismiss from the dataset will also be used to help aid in interpretation/application of the algorithm across the Landsat stack, so it is important to make notes of any patterns we might see in the outlier dataset.

## [1] "Classes represented in outliers:"

## [1] "darkNearShoreSediment"  "lightNearShoreSediment" "offShoreSediment"      
## [4] "openWater"

Okay, 84 of extreme outliers (>1.5*IQR) out of 1625 total labels across non-cloud classes.

Systemic contributor/scene errors

Are there any contributors that show up more than others in the outliers dataset?

vol_init	n_tot	n_out	percent_outlier
BGS	777	59	7.6
AMP	73	5	6.8
LRCP	181	8	4.4
KRW	49	2	4.1
HAD	432	10	2.3
FYC	98	NA	NA
LAE	15	NA	NA

These aren’t terrible. All below 10%, nothing egregious.

How many of these outliers are in specific scenes? For the purposes of comparison, we’ll drop the cloud groups.

date	vol_init	n_out	n_tot	percent_outlier
1988-11-23	BGS	28	67	41.8
1993-06-30	HAD	9	24	37.5
1998-10-02	BGS	15	98	15.3
2010-09-01	AMP	5	58	8.6
1998-08-31	LRCP	8	115	7.0
2000-08-04	BGS	5	74	6.8
1984-07-07	KRW	2	32	6.2
2006-07-04	BGS	3	58	5.2
2009-04-23	BGS	2	53	3.8
2004-08-15	BGS	2	55	3.6
2006-06-02	BGS	3	105	2.9
2007-05-04	HAD	1	57	1.8
2010-04-10	BGS	1	79	1.3

There are two scenes here that have a higher proportion of outliers (> 20% of total labels) - perhaps there is something about the AC in these particular scenes? or the general scene quality? Let’s look at the scene-level metadata:

date	vol_init	DATA_SOURCE_AIR_TEMPERATURE	DATA_SOURCE_ELEVATION	DATA_SOURCE_OZONE	DATA_SOURCE_PRESSURE	DATA_SOURCE_REANALYSIS	DATA_SOURCE_WATER_VAPOR	SENSOR_MODE_SLC	CLOUD_COVER_list	IMAGE_QUALITY_list	mean_cloud_cover	max_cloud_cover
1988-11-23	BGS	NCEP	GLS2000	TOMS	NCEP	MERRA-2	NCEP	ON	43; 3	9	23.0	43
1993-06-30	HAD	NCEP	GLS2000	TOMS	NCEP	MERRA-2	NCEP	ON	78; 63	9; 7	70.5	78

This isn’t terribly helpful, we still need to dig a little more.

How many bands are outliers when the data are aggregated back to label? If there is a large portion of outliers amongst the RGB bands (how users labeled data), there is probably a systemic problem. If the outliers are in singular bands, especially those that are not in the visible spectrum, we can dismiss the individual observations, and probably assert that the scene as a whole is okay to use in training. First pass, if there are 3 or more bands deemed outliers for a particular label, let’s look at the bands that are outliers:

date	class	vol_init	user_label_id	n_bands_out	bands_out
1988-11-23	openWater	BGS	1932	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1933	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1934	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1935	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1936	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1938	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1941	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1942	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1943	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1944	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1945	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1947	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1948	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1949	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1950	4	SR_B1; SR_B2; SR_B3; SR_B4
1988-11-23	openWater	BGS	1927	3	SR_B1; SR_B2; SR_B3
1988-11-23	openWater	BGS	1928	3	SR_B1; SR_B2; SR_B3
1988-11-23	openWater	BGS	1929	3	SR_B1; SR_B2; SR_B3
1988-11-23	openWater	BGS	1931	3	SR_B1; SR_B2; SR_B4
1988-11-23	openWater	BGS	1937	3	SR_B1; SR_B2; SR_B3
1988-11-23	openWater	BGS	1946	3	SR_B1; SR_B2; SR_B4
1998-08-31	darkNearShoreSediment	LRCP	847	3	SR_B4; SR_B5; SR_B7
1998-08-31	darkNearShoreSediment	LRCP	938	3	SR_B4; SR_B5; SR_B7
1998-10-02	offShoreSediment	BGS	1434	3	SR_B4; SR_B5; SR_B7
1998-10-02	offShoreSediment	BGS	1435	3	SR_B4; SR_B5; SR_B7
1998-10-02	offShoreSediment	BGS	1436	3	SR_B4; SR_B5; SR_B7
2006-06-02	lightNearShoreSediment	BGS	2146	3	SR_B4; SR_B5; SR_B7
2006-07-04	lightNearShoreSediment	BGS	140	3	SR_B4; SR_B5; SR_B7
2006-07-04	lightNearShoreSediment	BGS	157	3	SR_B4; SR_B5; SR_B7
2007-05-04	darkNearShoreSediment	HAD	3354	3	SR_B4; SR_B5; SR_B7

Let’s group by image date and volunteer and tally up the number of labels where at least 3 bands where outliers:

date	vol_init	n_labels
1988-11-23	BGS	21
1998-10-02	BGS	3
1998-08-31	LRCP	2
2006-07-04	BGS	2
2006-06-02	BGS	1
2007-05-04	HAD	1

At the very least, there are issues in the 1988-11-23 scene… but is that user error? Atmospheric correction?

This looks super hazy, so that may be the issue with this particular scene. Unfortunately, there is no way to know this from the scene metadata. Let’s see if the SR_ATMOS_OPACITY value is helpful at all:

outliers %>% 
  filter(date == "1988-11-23") %>% 
  pluck("SR_ATMOS_OPACITY") %>% 
  range(., na.rm = T)

## [1] 0.166 0.170

These values are pretty low (this is supposed to indicate hazy conditions), let’s look at cirrus confidcence across these labels, too:

outliers %>% 
    filter(date == "1988-11-23") %>% 
  pluck("cirrus_conf") %>% 
  range(., na.rm = T)

## [1] 0 0

Nothing here. How about cloud_conf?

outliers %>% 
    filter(date == "1988-11-23") %>% 
  pluck("cloud_conf") %>% 
  range(., na.rm = T)

## [1] 1 1

This is low confidence (bit value of 1). How about dialated clouds?

outliers %>% 
    filter(date == "1988-11-23") %>% 
  pluck("dialated_cloud") %>% 
  range(., na.rm = T)

## [1] 0 0

Nope.

While there doesn’t seem to be a systematic way of detecting the haze issue in this scene (or with the labeled pixels), it is clear that there is an issue with this scene. It’s also possible that the surrounding bright snow causes issues with the Rrs calculation.

QA Pixels

Do any of the labels have QA pixel indications of contamination? Let’s see if the medium certainty classification in the QA band is useful here:

LS5_for_class_analysis %>% 
  mutate(QA = case_when(cirrus_conf >=2 ~ "cirrus",
                   snowice_conf >= 2 ~ "snow/ice",
                   cloudshad_conf >= 2 ~ "cloud shadow",
                   cloud_conf >= 2 ~ "cloud",
                   TRUE ~ "clear")) %>% 
  group_by(QA) %>% 
  filter(class != "cloud") %>% 
  summarize(n_tot = n()) %>% 
  kable()

QA	n_tot
clear	1145
cloud	9
cloud shadow	62
snow/ice	11

And how about high certainty:

LS5_for_class_analysis %>% 
  mutate(QA = case_when(cirrus_conf >= 3 ~ "cirrus",
                   snowice_conf >= 3 ~ "snow/ice",
                   cloudshad_conf >= 3 ~ "cloud shadow",
                   cloud_conf >= 3 ~ "cloud",
                   TRUE ~ "clear")) %>% 
  group_by(QA) %>% 
  filter(class != "cloud") %>% 
  summarize(n_tot = n()) %>% 
  kable()

QA	n_tot
clear	1147
cloud	7
cloud shadow	62
snow/ice	11

What about low confidence?

LS5_for_class_analysis %>% 
  mutate(QA = case_when(snowice_conf >= 1 ~ "snow/ice",
                   cloudshad_conf >= 1 ~ "cloud shadow",
                   cirrus_conf >= 1 ~ "cirrus",
                   cloud_conf >= 1 ~ "cloud",
                   TRUE ~ "clear")) %>% 
  group_by(QA) %>% 
  filter(class != "cloud") %>% 
  summarize(n_tot = n()) %>% 
  kable()

QA	n_tot
snow/ice	1227

Low confidence is NOT helpful! Let’s move forward with medium confidence and look at the flagged data from all classes except cloud:

LS5_qa_flagged <- LS5_for_class_analysis %>% 
  filter((cirrus_conf >= 2 |
           snowice_conf >= 2 |
           cloudshad_conf >= 2 |
           cloud_conf >= 2),
    class != "cloud") %>%
  group_by(date, vol_init) %>% 
  summarise(n_qa_flagged = n()) %>% 
  arrange(-n_qa_flagged)
LS5_tot <- LS5_for_class_analysis %>% 
  group_by(date, vol_init) %>% 
  filter(class != "cloud") %>% 
  summarise(n_tot_labels = n())
left_join(LS5_qa_flagged, LS5_tot) %>% 
  mutate(percent_qa_flagged = round(n_qa_flagged/n_tot_labels*100, 1)) %>% 
  arrange(-percent_qa_flagged) %>% 
  kable()

date	vol_init	n_qa_flagged	n_tot_labels	percent_qa_flagged
1987-11-05	HAD	28	41	68.3
1993-06-30	HAD	11	24	45.8
1990-06-06	HAD	8	25	32.0
1998-10-02	BGS	24	98	24.5
2007-05-04	HAD	4	57	7.0
2004-08-15	BGS	3	55	5.5
2009-04-23	BGS	1	53	1.9
2011-10-06	HAD	2	111	1.8
1988-11-23	BGS	1	67	1.5

We don’t want to be using data that has QA flags for any pixel that isn’t labeled cloud. Let’s look at the four images with >20% QA flag labels:

1987-11-05

LS5_for_class_analysis %>% 
  filter(date == "1987-11-05", class != "cloud",
         (cirrus_conf >= 2 |
           snowice_conf >= 2 |
           cloudshad_conf >= 2 |
           cloud_conf >= 2)) %>% 
  select(class, SR_ATMOS_OPACITY, cirrus_conf, cloud_conf, cloudshad_conf, snowice_conf) %>% 
  kable()

class	SR_ATMOS_OPACITY	cirrus_conf	cloud_conf	cloudshad_conf	snowice_conf
openWater	0.080	0	1	3	1
openWater	0.062	0	1	3	1
openWater	0.055	0	1	3	1
openWater	0.059	0	1	3	1
openWater	0.066	0	1	3	1
openWater	0.054	0	1	3	1
openWater	0.071	0	1	3	1
openWater	0.059	0	1	3	1
openWater	0.063	0	1	3	1
openWater	0.087	0	1	3	1
openWater	0.072	0	1	3	1
openWater	0.071	0	1	3	1
openWater	0.072	0	1	3	1
openWater	0.074	0	1	3	1
openWater	0.080	0	1	3	1
lightNearShoreSediment	0.073	0	1	3	1
lightNearShoreSediment	0.074	0	1	3	1
lightNearShoreSediment	0.079	0	1	3	1
lightNearShoreSediment	0.042	0	1	3	1
lightNearShoreSediment	0.034	0	1	3	1
lightNearShoreSediment	0.037	0	1	3	1
lightNearShoreSediment	0.041	0	1	3	1
darkNearShoreSediment	0.075	0	1	3	1
darkNearShoreSediment	0.075	0	1	3	1
darkNearShoreSediment	0.025	0	1	3	1
darkNearShoreSediment	0.026	0	1	3	1
darkNearShoreSediment	0.026	0	1	3	1
darkNearShoreSediment	0.034	0	1	3	1

These are entirely cloud shadow flags, which makes sense given the extent and pattern of clouds. This scene does not seem contaminated with haze (which is confimred) by SR_ATMOS_OPACITY values < 0.1.

1993-06-30

LS5_for_class_analysis %>% 
  filter(date == "1993-06-30", class != "cloud",
         (cirrus_conf >= 2 |
           snowice_conf >= 2 |
           cloudshad_conf >= 2 |
           cloud_conf >= 2)) %>% 
  select(class, SR_ATMOS_OPACITY, cirrus_conf, cloud_conf, cloudshad_conf, snowice_conf) %>% 
  kable()

class	SR_ATMOS_OPACITY	cirrus_conf	cloud_conf	cloudshad_conf	snowice_conf
openWater	0.663	0	1	3	1
lightNearShoreSediment	0.379	0	1	3	1
lightNearShoreSediment	0.373	0	3	1	1
darkNearShoreSediment	0.439	0	1	1	3
darkNearShoreSediment	0.442	0	1	1	3
darkNearShoreSediment	0.415	0	1	1	3
darkNearShoreSediment	0.413	0	1	1	3
darkNearShoreSediment	0.422	0	1	1	3
darkNearShoreSediment	0.442	0	1	1	3
darkNearShoreSediment	0.422	0	1	1	3
darkNearShoreSediment	0.411	0	1	1	3

Most of these are high opacity values, indicating some cirrus-type contamination. Additionally, there is some mis classification of snow and ice here. As indicated by the opacity values and the qa flags, these pixels should be dropped. SR_ATOMS_OPACITY greater than 0.3 indicate haze or other cloud presence.

LS5_for_class_analysis %>% 
  filter(date == "1993-06-30", class != "cloud") %>% 
  pluck("SR_ATMOS_OPACITY") %>% 
  range()

## [1] 0.373 0.663

1990-06-06

LS5_for_class_analysis %>% 
  filter(date == "1990-06-06", class != "cloud",
         (cirrus_conf >= 2 |
           snowice_conf >= 2 |
           cloudshad_conf >= 2 |
           cloud_conf >= 2)) %>% 
  select(class, SR_ATMOS_OPACITY, cirrus_conf, cloud_conf, cloudshad_conf, snowice_conf) %>% 
  kable()

class	SR_ATMOS_OPACITY	cirrus_conf	cloud_conf	cloudshad_conf	snowice_conf
openWater	0.267	0	1	3	1
openWater	0.268	0	1	3	1
openWater	0.236	0	1	3	1
openWater	0.241	0	1	3	1
openWater	0.080	0	1	3	1
lightNearShoreSediment	0.391	0	3	1	1
lightNearShoreSediment	0.388	0	1	3	1
darkNearShoreSediment	0.264	0	1	3	1

Mostly cloud and cloud shadow here. Atmospheric opacity is a bit lower, but still some that will need to be removed from the training dataset.

1998-10-02

LS5_for_class_analysis %>% 
  filter(date == "1998-10-02", class != "cloud",
         (cirrus_conf >= 2 |
           snowice_conf >= 2 |
           cloudshad_conf >= 2 |
           cloud_conf >= 2)) %>% 
  select(class, SR_ATMOS_OPACITY, cirrus_conf, cloud_conf, cloudshad_conf, snowice_conf) %>% 
  kable()

class	SR_ATMOS_OPACITY	cirrus_conf	cloud_conf	cloudshad_conf	snowice_conf
openWater	0.270	0	1	3	1
lightNearShoreSediment	0.284	0	1	3	1
lightNearShoreSediment	0.286	0	1	3	1
lightNearShoreSediment	0.251	0	1	3	1
lightNearShoreSediment	0.241	0	1	1	3
lightNearShoreSediment	0.295	0	3	3	1
lightNearShoreSediment	0.295	0	3	3	1
lightNearShoreSediment	0.296	0	2	1	1
lightNearShoreSediment	0.296	0	2	1	3
darkNearShoreSediment	0.213	0	1	3	1
darkNearShoreSediment	0.213	0	1	3	1
darkNearShoreSediment	0.213	0	3	3	1
darkNearShoreSediment	0.213	0	1	3	1
offShoreSediment	0.219	0	1	3	1
offShoreSediment	0.278	0	1	3	1
offShoreSediment	0.271	0	1	3	1
offShoreSediment	0.251	0	3	1	1
offShoreSediment	0.282	0	2	3	1
offShoreSediment	0.236	0	1	3	1
offShoreSediment	0.248	0	3	1	1
offShoreSediment	0.218	0	3	1	1
offShoreSediment	0.275	0	3	1	1
offShoreSediment	0.293	0	2	1	1
offShoreSediment	0.293	0	3	1	1

It’s clear there is a bit of haze in this image, that is confirmed by opacity values, though the opacity is till below the 0.3 cutoff. Using the QA bits here to remove medium and high confidence QA flags should resolve any contamination. Let’s also look at the range of atmoshperic opacity across all labels in this scene:

LS5_for_class_analysis %>% 
  filter(date == "1998-10-02", class != "cloud") %>% 
  pluck("SR_ATMOS_OPACITY") %>% 
  range()

## [1] 0.034 0.296

All below the 0.3 threshold.

And now for the outliers:

outliers %>% 
  mutate(QA = case_when(snowice_conf >= 2 ~ "snow/ice",
                   cloudshad_conf >= 2 ~ "cloud shadow",
                   cirrus_conf >= 2 ~ "cirrus",
                   cloud_conf >= 2 ~ "cloud",
                   TRUE ~ "clear")) %>% 
  group_by(QA) %>% 
  filter(class != "cloud") %>% 
  summarize(n_out_tot = n()) %>% 
  kable()

QA	n_out_tot
clear	67
cloud	7
cloud shadow	4
snow/ice	6

And let’s look at atmospheric opacity:

outliers %>% 
  filter(class != "cloud") %>% 
  pluck("SR_ATMOS_OPACITY") %>% 
  range(na.rm = T)

## [1] 0.027 0.498

Clouds

How many of these outliers have near-pixel clouds (as measured by ST_CDIST)?

There are 12 labels (14.3% of oultiers) that aren’t “cloud” in the outlier dataset that have a cloud distance <500m and 126 labels (7.8%) in the whole dataset that have a cloud distance <500m. Since this is about the same portion of labels (or they are not severely disproportionate), I don’t think this is terribly helpful.

How many of the outliers have high cloud cover, as reported by the scene-level metadata? Note, we don’t have the direct scene cloud cover associated with individual labels, rather a list of the scene level cloud cover values associated with the AOI.

The outlier dataset contains 9 (10.7%) where the max cloud cover was > 75% and 9 (10.7%) where the mean cloud cover was > 50%. The filtered dataset contains 24 (1.5%) where max was >75% and 47 (2.9%) where the mean cloud cover was > 50%. Neither of these metrics seem helpful in elimination of labels from outlier scenes.

Training dataset implications

Based on the above review, any label with SR_ATMOS_OPACITY value >= 0.3, or a QA flag for clouds, cloud shadow, or snow ice should be eliminated from this dataset.

LS5_training_labels <- LS5_for_class_analysis %>% 
  filter(class == "cloud" |
           (SR_ATMOS_OPACITY < 0.3 &
            cloud_conf < 2 &
            cloudshad_conf <2 & 
            snowice_conf <2))

Testing for inter-class differences

We do want to have an idea of how different the classes are, in regards to band data. While there are a bunch of interactions that we could get into here, for the sake of this analysis, we are going to analyze the class differences by band.

Kruskal-Wallis assumptions:

1. Data are non-Normal or have a skewed distribution
2. There must be at least two independent groups.
3. Data have a similar distribution across groups.
4. Data are independent, the groups shouldn’t have a relationship to one another
5. Each group should contain at least 5 observations

ANOVA assumptions:

1. data are distributed normally
2. data are independent
3. variance across groups is similar

We can’t entirely assert sample independence and we know that variance and distribution is different for “cloud” labels, but those data also are visibly different from the other classes.

In order to systematically test for differences between classes and be able to intepret the data, we will need to know some things about our data:

1. Are the data normally distributed (Shapiro-Wilkes)?
2. Are there outliers that may impact interpretation?
3. If data is non-normal, perform Kruskal-Walis test; otherwise ANOVA
4. if the null is rejected (and there is a difference in at least one class), perform post-hoc test for pairwise comparison (Dunn test for both)

With this workflow, most classes are statistically different - below are the cases where the pairwise comparison were not deemed statistically significant:

## # A tibble: 10 × 9
##    band  group1         group2    n1    n2 statistic       p  p.adj p.adj.signif
##    <chr> <chr>          <chr>  <int> <int>     <dbl>   <dbl>  <dbl> <chr>       
##  1 SR_B1 darkNearShore… offSh…   135   273    2.77   0.00552 0.0552 ns          
##  2 SR_B2 darkNearShore… offSh…   135   273    0.0529 0.958   1      ns          
##  3 SR_B4 darkNearShore… light…   135   257    2.12   0.0338  0.338  ns          
##  4 SR_B5 darkNearShore… light…   135   257    2.31   0.0211  0.211  ns          
##  5 SR_B5 darkNearShore… offSh…   135   273   -1.72   0.0861  0.861  ns          
##  6 SR_B5 offShoreSedim… openW…   273   387   -2.54   0.0111  0.111  ns          
##  7 SR_B7 darkNearShore… light…   135   257    2.00   0.0458  0.458  ns          
##  8 SR_B7 darkNearShore… offSh…   135   273   -1.36   0.174   1      ns          
##  9 SR_B7 darkNearShore… openW…   135   387   -2.38   0.0172  0.172  ns          
## 10 SR_B7 offShoreSedim… openW…   273   387   -1.20   0.229   1      ns

There is some consistency here: “darkNearShoreSediment” is often not different from other sediment types by band. It is entirely possible that band interactions overpower these non-significant differences.

Let’s look at the boxplots for the training labels, dropping the cloud labels to see the ranges better:

LS5_training_labels_no_clouds <- LS5_training_labels %>% 
  filter(class != "cloud")
pmap(.l = list(data = list(LS5_training_labels_no_clouds),
               data_name = list("LANDSAT_5"),
               band = LS57_ee),
     .f = make_class_comp_plot)

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There are 26 outliers in SR_B1, 7 in SR_B2, 8 in SR_B3, 20 in SR_B4, 42 in SR_B5, and 40 in SR_B7.

DNSS: dark near shore sediment, LNSS: light near shore sediment, OSS: offshore sediment

There are definitely some varying patterns here, let’s zoom in on the sediment classes.

DNSS: dark near shore sediment, LNSS: light near shore sediment, OSS: offshore sediment

Okay, this seems sensical as to why there is no significant difference in many of the band-level “darkNearShoreSediment” labels - there’s a lot of overlap in ranges. Looking at these scatter plot matrices, I do think there are likely different enough patterns when considering multiple bands that ML should be able to pick up on subtle differences.

Export the training labels

Things to note for Landsat 5:

• pixels with QA flags should be dismissed from model application

• pixels with SR_ATMOS_OPACITY > 0.3 should be dismissed from model application

write_rds(LS5_training_labels, paste0("data/labels/LS5_labels_for_tvt_", outlier_version, ".RDS"))